Response to inhaled granulocyte-macrophage colony-stimulating factor in patient with mild-to-moderate autoimmune pulmonary alveolar proteinosis-24 months of follow-up.

INTRODUCTION: Autoimmune pulmonary alveolar proteinosis (aPAP) is a disease caused by IgG antibodies against granulocyte-macrophage colony-stimulating factor (GM-CSF). Whole lung lavage (WLL) allows to remove the lipo-proteinaceous material accumulated by the poor clearance of alveolar surfactant. However, it is a complex technique that is not exempt from complications, and in some cases, the patients are refractory, requiring the performance of several WLLs spaced apart in time. MATERIALS AND METHODS: We present the clinical, functional, and radiological evolution after 24 months of follow-up of a patient diagnosis of aPAP refractory to WLL, with performed three therapeutic WLLs spaced 16 and 36 months and serious potentially fatal complications in the last one. RESULTS AND DISSCUSION: After 24 months, no adverse effects have appeared and the great clinical, functional and radiological response is maintained. The patient has been successfully treated with inhaled recombinant human GM-CSF sargramostim.

Comparison of Three Comorbidity Measures for Predicting In-Hospital Death through a Clinical Administrative Nacional Database.

BACKGROUND: Various authors have validated scales to measure comorbidity. However, the prognosis capacity variation according to the comorbidity measurement index used needs to be determined in order to identify which is the best predictor. AIMS: To quantify the differences between the Charlson (CCI), Elixhauser (ECI) and van Walraven (WCI) comorbidity indices as prognostic factors for in-hospital mortality and to identify the best comorbidity measure predictor. METHODS: A retrospective observational study that included all hospitalizations of patients over 18 years of age, discharged between 2017 and 2021 in the hospital, using the Minimum Basic Data Set (MBDS). We calculated CCI, ECI, WCI according to ICD-10 coding algorithms. The correlation and concordance between the three indices were evaluated by Spearman's rho and Intraclass Correlation Coefficient (ICC), respectively. The logistic regression model for each index was built for predicting in-hospital mortality. Finally, we used the receiver operating characteristic (ROC) curve for comparing the performance of each index in predicting in-hospital mortality, and the Delong method was employed to test the statistical significance of differences. RESULTS: We studied 79,425 admission episodes. The 54.29% were men. The median age was 72 years (interquartile range [IQR]: 56-80) and in-hospital mortality rate was 4.47%. The median of ECI was = 2 (IQR: 1-4), ICW was 4 (IQR: 0-12) and ICC was 1 (IQR: 0-3). The correlation was moderate: ECI vs. WCI rho = 0.645, p < 0.001; ECI vs. CCI rho = 0.721, p < 0.001; and CCI vs. WCI rho = 0.704, p < 0.001; and the concordance was fair to good: ECI vs. WCI Intraclass Correlation Coefficient type A (ICCA) = 0.675 (CI 95% 0.665-0.684) p < 0.001; ECI vs. CCI ICCA = 0.797 (CI 95% 0.780-0.812), p < 0.001; and CCI vs. WCI ICCA = 0.731 (CI 95% 0.667-0.779), p < 0.001. The multivariate regression analysis demonstrated that comorbidity increased the risk of in-hospital mortality, with differences depending on the comorbidity measurement scale: odds ratio [OR] = 2.10 (95% confidence interval [95% CI] 2.00-2.20) p > |z| < 0 using ECI; OR = 2.31 (CI 95% 2.21-2.41) p > |z| < 0 for WCI; and OR = 2.53 (CI 95% 2.40-2.67) p > |z| < 0 employing CCI. The area under the curve [AUC] = 0.714 (CI 95% 0.706-0.721) using as a predictor of in-hospital mortality CCI, AUC = 0.729 (CI 95% 0.721-0.737) for ECI and AUC = 0.750 (CI 95% 0.743-0.758) using WCI, with statistical significance (p < 0.001). CONCLUSION: Comorbidity plays an important role as a predictor of in-hospital mortality, with differences depending on the measurement scale used, the van Walraven comorbidity index being the best predictor of in-hospital mortality.